Graffiti vs. Unistrokes: An Empirical
Comparison

Dept. of Computer Science and Engineering
York University Toronto, Canada M3J 1P3
{stevenc,mack}@cse.yorku.ca

ABSTRACTUnistrokes and Graffiti are stylus-based text entry techniques. While
Unistrokes is recognized in academia, Graffiti is commercially
prevalent in PDAs. Though numerous studies have investigated the
usability of Graffiti, none exists to compare its long-term
performance with that of Unistrokes. This paper presents a
longitudinal study comparing entry speed, correction rate, stroke
duration, and preparation (i.e., inter-stroke) time of these two
techniques. Over twenty fifteen-phrase sessions, performance
increased from 4.0 wpm to 11.4 wpm for Graffiti and from 4.1 wpm
to 15.8 wpm for Unistrokes. Correction rates were high for both
techniques. However, rates for Graffiti remained relatively consistent
at 26%, while those for Unistrokes decreased from 43% to 16%.

INTRODUCTION

Stylus-based entry techniques facilitate one-handed text input on
portable systems, such as PDAs and tablet PCs. The user employs a
pen-like stylus to "write" on a touch screen or digitizing tablet. The
resulting "digital ink" can form gestures that are interpreted as text.
After introducing Graffiti and Unistrokes, we detail a user study to
evaluate and compare them. We then present the results and elaborate
on the findings.

Graffiti

Created and marketed by Palm, Inc. (www.palm.com), Graffiti (now
Graffiti2) allows text entry with a stylus. A predominant feature of
the Graffiti gesture alphabet (Figure 1, top) is that each stroke
resembles its assigned Roman letter. This is intended to facilitate
learning. Support for this was found in a previous study, where users
demonstrated 97% accuracy after only five minutes of practice [6].
Other alphabets (e.g., Jot) employ a similar approach, but incorporate
multiple, subtly different gestures for each Roman letter. This
increases the chance that gestures match the user's own handwriting,
further facilitating proficiency. Graffiti is common in both
commercial PDAs and in academic research [3].

Unistrokes

First introduced at the ACM SIGCHI conference in 1993, Unistrokes
is a gesture alphabet for stylus-based text entry [2]. The term
"unistrokes" characterizes all single-stroke gesture alphabets (Graffiti
included). However, in this paper, "Unistrokes" refers specifically to
the original gesture alphabet (Figure 1, bottom).
The single-stroke nature of each gesture allows entry without the user
attending to the writing area [2]. Furthermore, the alphabet's strokes
are well distinguished in "sloppiness space" [2], allowing for accurate
recognition of not-so-accurate input.

Unlike Graffiti, Unistrokes gestures bare little resemblance to Roman
letters. However, each letter is assigned a short stroke, with frequent
letters (e.g., E, A, T, I, R) associated with a straight line. Unistrokes
is analogous to touch-typing with a keyboard, as practice will result
in high-speed, "eyes-free" input [2].

METHOD

Participants

The ten paid participants (four males and six females) were students
at the local university. They were recruited by posting flyers on
campus. Ages ranged from 19 to 30 years (mean = 23; SD = 3.07). Of
the ten, two were left-handed. Two used stylus-based devices at least
once a week, three used them less frequently, and five had never
previously used such devices. Nine frequently took hand-written
lecture notes, while one favoured typing them. All possessed the
dexterity to operate a stylus easily. None was familiar with either
Unistrokes or Graffiti.

Apparatus

Figure 2 depicts the Java program used for gesture recognition and
gathering text entry metrics. The topmost text area displayed the
presented phrase, and the lower one the participant's transcribed text.
The rectangle below is the stroke recognition area (SRA). The
recognizer was borrowed from an earlier study [3]. Below the SRA is
the enter button to terminate entry of a phrase. Above the SRA, reside
the chart button and the backspace button. The absence of a
backspace gesture in the Unistrokes paper [2] motivated the use of a
backspace button.

Figure 2: The interface used to gather text-entry metrics.

The workstation for the experiment was a Pentium 4 530 (3 GHz)
system with a Wacom PL-400 digitizing tablet, which integrates a
1024 × 768 LCD display. The workstation ran the text entry program
using version 1.5.0 of the Java Runtime Environment. The program
window was maximized to avoid extraneous onscreen stimuli. No
additional applications were running. The study took place in a quiet
office environment.

Procedure

At the beginning of each session, participants were given up to two
minutes (five for the first session) to study a chart (similar to
Figure 1) of the assigned gesture alphabet. By pressing the chart
button, participants could view the chart in a popup modal dialog
during the session. Since PDAs come with gesture alphabet reference
cards, we believe this aided external validity.

After being instructed to "enter the presented phrases as quickly and
accurately as possible", participants used the stylus to enter text using
gestures of the assigned technique. Gestures were entered in the SRA
of the interface. The presented phrase remained visible to the
participant throughout input. This aimed to eliminate errors due to
spelling mistakes, and delays caused by forgetting a memorized
phrase. Participants were allowed to rest between phrases. To
encourage attention to the task, participants were required to correct
all errors by using the backspace button and re-entering incorrect or
mis-recognized gestures. Phrases with errors remaining were
immediately repeated. Such erroneous phrase entries were not
analyzed.

Design

The experiment was a 2 × 20 factorial design. A between-subjects
factor, Input Technique, had two levels: Graffiti and Unistrokes. Use
of a between-subjects factor eliminated any interference effects
between the two techniques. A repeated measures factor, Session,
represented twenty sessions of text entry. The dependent variables
(and units) were Entry Speed (words per minute), Correction Rate
(%), Chart Views (seconds per phrase), Stroke Duration
(milliseconds), and Preparation Time (milliseconds).

The ten participants were randomly divided into two equal groups –
one for each gesture alphabet. They made session appointments at
their convenience of about fifteen minutes each. Sessions were
separated by at least one hour but not more than three days. Each
session involved fifteen phrases of text entry. Phrases were chosen
randomly (without replacement) from a 500-phrase set [4]. The few
instances of capital letters were converted to lowercase. The study
lasted six weeks.

Entry time for each phrase was measured from the first pen-down
event to the last pen-up event. It also included any time spent viewing
the gesture alphabet chart. Using the accepted word length of five
characters (including spaces) [8, p. 182], entry speed was calculated
by dividing the phrase length by the entry time (in seconds),
multiplying by sixty (i.e., seconds in a minute), and dividing by five
(i.e., word length). Because participants were required to correct all
errors, error rate was inherently zero percent. Instead, we calculated
correction rate, defined as the number of backspace button presses
per phrase divided by the length of the phrase.

RESULTS AND DISCUSSION

Performance

Figure 3 shows the results for Entry Speed. Learning effects were
observed in both the Graffiti and the Unistrokes groups. During the
first session, Graffiti users entered text at 4.0 wpm (SD = 1.44), while
Unistrokes users entered at 4.1 wpm (SD = 2.18). By the twentieth
session, Graffiti users entered at an average of 11.4 wpm (SD = 3.60),
while Unistrokes users entered at 15.8 wpm (SD = 4.02).

Correction Rate and Chart Views

Figure 4 illustrates the change in Correction Rate over the twenty
sessions. Rates for Graffiti remained steady, averaging 26.2%
(SD = 2.6). Those for Unistrokes decreased from 43.4% (SD = 16.4)
for session one to 16.3% (SD = 10.0) for session twenty. Although
these rates seem high, they can be explained as follows.

Figure 4: Correction Rate results.

In addition to single backspace events in the logs, consecutive
backspace events were also evident. These occurred because
participants tended to view the writing area, as consistent with
handwriting. Consequently, participants often missed errors. Once
noticed, he or she repeatedly pressed the backspace button to perform
the correction, deleting correct characters in the process.

We also observed participants making repeated attempts to enter and
correct a problem gesture. Instead of viewing the alphabet chart,
participants favoured a guess-and-check approach. Viewing of the
alphabet chart varied considerably. For the first session, Graffiti users
spent an average of 4.0 seconds per phrase (SD = 8.6). For Unistrokes
user, the average was much longer, at 12.5 seconds per phrase
(SD = 23.6). For subsequent sessions, average chart viewing time for
Graffiti users dropped to below one second per phrase, but the
standard deviation remained high. The same was true for Unistroke
users during the third and subsequent sessions.

Stroke Duration

Figure 5 displays the change in Stroke Duration (i.e., the time from
pen-down to pen-up) over the twenty sessions. Due to their short and
simple strokes, Unistrokes gestures were executed significantly faster
than those of Graffiti
(F1,8 = 8.21, p < .05).

Figure 5: Stroke Duration results.

Cao and Zhai devised a model of gesture composition by predicting
the stroke duration of primitive components [1]. To evaluate it, they
conducted an empirical study using Graffiti and Unistrokes. Table 1
presents a summary of their results. It also includes the stroke
durations from this study, averaged over twenty sessions. Both
studies yielded stroke durations much lower than the model's
prediction. However, the empirical Unistrokes-to-Graffiti stroke
duration ratios differ by only 0.32%! The discrepancy in the actual
durations can be attributed to the additional practice afforded by this
longitudinal study, compared to Cao and Zhai's small-scale [1,
p. 1501] experiment.

Technique

ThisStudy

ExperimentData [1]

ModelPrediction [1]

Graffiti (A)

459

591

1125

Unistrokes (B)

284

365

622

Ratio (B/A)

0.620

0.618

0.553

Table 1. Stroke time data from this study and Cao & Zhai [1].

Preparation Time

Figure 6 illustrates the change in Preparation Time (i.e., the time
between strokes) over the twenty sessions. Again, the two curves
exhibit obvious learning effects. However, while the improvement
over the twenty sessions was statistically significant
(F19,152 = 54.20,
p < .0001), the main effect of Input Technique on Preparation Time
was not
(F1,8 = 1.57, p > .05).

Figure 6: Preparation Time results.

The similarity of Graffiti to English suggests a significantly lower
preparation time. Indeed, participants in the Graffiti group cited this
feature as conducive to his or her performance. However, subtle
differences between one's personal handwriting style and the Graffiti
alphabet might diminish Graffiti's mnemonic associations. For
example, a common source of error in this study (and another [6])
was the addition of superfluous down-strokes in completing the
letters G and U. While other gesture alphabets associate subtly
different gestures with a single letter, the single gesture per letter
design of Unistrokes makes Graffiti a better candidate for
comparison.

Another explanation for the similar preparation times is Graffiti's
similarity with uppercase letters. Accounting for letter frequency,
only 33% of lowercase Roman letters resemble their corresponding
Graffiti gesture [6]. As typical of text entry experiments [4, 5], the
current study employed phrases with only lowercase letters.
Therefore, the presentation of such letters might have interfered with
the strokes' mnemonic association with uppercase letters. This
Stroop-like effect [7] would result in increased preparation time with
the Graffiti alphabet. The Unistrokes alphabet lacks resemblance to
Roman letters, and therefore is not susceptible to such an effect.
Instead, its results can best be explained by participants' inexperience
with the technique.

CONCLUSION

Over twenty fifteen-phrase sessions, text entry speed in the Graffiti
group increased from 4.0 wpm to 11.4 wpm. During the same time,
text entry speed in the Unistrokes group increased from 4.1 wpm to
15.8 wpm. However, an analysis of variance yielded a lack of
statistical difference in entry speed between the two techniques.
Participants often performed unnecessary deletions, resulting in high
correction rates. In addition, the duration of gesture chart views
decreased quickly, but varied widely between participants.
Inter-stroke time between the two groups was similar, but the significant
difference in stroke duration favoured Unistrokes.
The Graffiti alphabet's recognisability endears itself to novice users.
However, this study shows that investing the same time learning
Unistrokes can result in significantly faster stroke time and higher
text entry speed.

ACKNOWLEDGMENTS

We wish to thank the CHI reviewers for their constructive feedback.
This research was funded by the Natural Sciences and Engineering
Research Council of Canada.